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Field note·Agentic Systems & Orchestration·4 min read

MCP in Production — What Works and What Breaks

The protocol is solid and boring, which is what you want. Almost every production problem sits one layer above it.

MCP in Production — What Works and What Breaks

Model Context Protocol does what it promises: write a server once and it works across hosts, with schemas travelling alongside the tools. Adopting it takes an afternoon. Getting a model to use your tools well is the actual project.

What works

  • One server, many hosts — no per-client rewrites.
  • Schemas travel with the tools, so the host validates arguments before anything executes.
  • Transport is deliberately boring; the interesting failures are never there.
  • Errors come back as values rather than exceptions, so an agent can reason about them.

What breaks

01Too many toolsPast roughly 15–20, selection accuracy falls off. The model pattern-matches on names and descriptions rather than reading a registry.
02Vague descriptionsA tool described as “gets data” will be called for everything. Descriptions are prompt engineering, not documentation.
03Overlapping toolsTwo tools that could both plausibly answer means the wrong one gets picked a large share of the time.
04Silent retriesWithout limits, a confused model calls the same tool repeatedly and you find out from the bill.

Three fixes worth doing early

  • Merge overlapping tools and name them for the intent they serve, not the endpoint they wrap.
  • Write descriptions for the model: state when to use it and when not to. The negative case prevents more misfires than the positive one.
  • Rate limit per session so a confused agent fails cheaply rather than expensively.
What to take away
  • Adopting the protocol is easy; tool design is the real work.
  • Keep the tool count low — accuracy degrades past ~15–20.
  • Write descriptions that say when NOT to use a tool.
  • Rate limit per session so confusion fails cheaply.

Field notes on building production AI systems — collected, verified and written up so they are useful to anyone working on the same problems.

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